Weights & Biases

Weights & Biases, founded in 2017 by Lukas Biewald, Chris Van Pelt, and Shawn Lewis, offers a suite of tools designed to assist machine learning engineers in tracking experiments, managing datasets, and collaborating effectively. Their platform includes features for visualizing model performance, versioning datasets, and automating training processes, aiming to streamline the machine learning development lifecycle.

The company's flagship products are W&B Models and W&B Weave. W&B Models enables users to train, fine-tune, and manage AI models, providing a comprehensive system for experiment tracking and model versioning. W&B Weave focuses on LLM observability, allowing developers to evaluate, monitor, and iterate on large language model applications, facilitating the development of AI-powered applications with confidence.

As of August 2023, Weights & Biases has secured a total of $250 million in funding across multiple rounds, with a valuation of $1.25 billion. Notable investors include Coatue, Insight Partners, Felicis Ventures, and Bond Capital. The company has established partnerships with leading AI organizations, including OpenAI, NVIDIA, and Cohere, and serves over 1,300 customers, including more than 30 foundation model builders. In May 2025, Weights & Biases was acquired by CoreWeave, a move that is expected to enhance its capabilities in the generative AI sector.

Headquartered at 400 Alabama Street Suite 202, San Francisco, California, 94110, United States, Weights & Biases employs between 200 and 500 individuals. The company operates in the AI infrastructure sector, providing essential tools for machine learning practitioners to build, track, and deploy models efficiently. Its main competitors include DataRobot, Abacus.AI, and Anyscale, which also offer platforms for machine learning operations and model management.

In addition to its core offerings, Weights & Biases has expanded its product suite to include W&B Prompts, a tool designed to help users monitor and evaluate the performance of large language models, further solidifying its position in the AI development ecosystem. The company's commitment to providing comprehensive, user-friendly tools has made it a preferred choice for machine learning teams seeking to enhance their development workflows and model performance.